{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn; \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import pylab as pl\n",
    "\n",
    "seaborn.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mAFCzCjsHi1y3K3EljEXYBABqVn56Y5HrpiWuhLEImwBAzVowJx+t+ab9rmltbor5s2dV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\nAEAy/z+Ybr+ENi1QeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca28898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "X = np.random.random(size=(20, 1))\n",
    "y = 3 * X.squeeze() + 2 + np.random.randn(20)\n",
    "\n",
    "plt.plot(X.squeeze(), y, 'o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mAFCzCjsHi1y3K3EljEXYBABqVn56Y5HrpiWuhLEImwBAzVowJx+t+ab9rmltbor5s2dV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\nAEAy/z+Ybr+ENi1QeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6a5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "X = np.random.random(size=(20, 1))\n",
    "y = 3 * X.squeeze() + 2 + np.random.randn(20)\n",
    "\n",
    "plt.plot(X, y, 'o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5488135 ],\n",
       "       [ 0.71518937],\n",
       "       [ 0.60276338],\n",
       "       [ 0.54488318],\n",
       "       [ 0.4236548 ],\n",
       "       [ 0.64589411],\n",
       "       [ 0.43758721],\n",
       "       [ 0.891773  ],\n",
       "       [ 0.96366276],\n",
       "       [ 0.38344152],\n",
       "       [ 0.79172504],\n",
       "       [ 0.52889492],\n",
       "       [ 0.56804456],\n",
       "       [ 0.92559664],\n",
       "       [ 0.07103606],\n",
       "       [ 0.0871293 ],\n",
       "       [ 0.0202184 ],\n",
       "       [ 0.83261985],\n",
       "       [ 0.77815675],\n",
       "       [ 0.87001215]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FzIiiZkRbG7YUqcuNteqw+e///u/69V//dRmGIdu2ZRiG3vzmN+szn/mMG/0BAMQFP0Ap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\ncU4GftdoavgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1154a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_fit = np.linspace(0, 1, 100)[:, np.newaxis]\n",
    "y_fit = model.predict(X_fit)\n",
    "\n",
    "plt.plot(X.squeeze(), y, 'o')\n",
    "plt.plot(X_fit.squeeze(), y_fit);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2B+BCMAUAtCvb2avK+P/2y1Z81X02cXhvPTY9Ud2o7QE0QTAFALSLyut2vb/D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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc73d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_fit = np.linspace(0, 1, 100)[:, np.newaxis]\n",
    "y_fit = model.predict(X_fit)\n",
    "\n",
    "\n",
    "plt.plot(X_fit, y_fit);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2B+BCMAUAtCvb2avK+P/2y1Z81X02cXhvPTY9Ud2o7QE0QTAFALSLyut2vb/D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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8bc7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_fit = np.linspace(0, 1, 100)[:, np.newaxis]\n",
    "y_fit = model.predict(X_fit)\n",
    "\n",
    "\n",
    "plt.plot(X_fit.squeeze(), y_fit);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
